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I
nt. J. Entrepreneurship and Small Business, Vol. 43, No. 4, 2021 517
Copyright © 2021 Inderscience Enterprises Ltd.
Who earns more: job creators, solo-entrepreneurs
or employees? Empirical evidence from Visegrad
countries
Ondřej Dvouletý*
Department of Entrepreneurship,
Faculty of Business Administration,
University of Economics, Prague,
W. Churchill Sq. 4, 130-67 Prague 3, Czech Republic
Email: ondrej.dvoulety@vse.cz
*Corresponding author
David Anthony Procházka
Department of Management,
Faculty of Business Administration,
University of Economics, Prague,
W. Churchill Sq. 4, 130-67 Prague 3, Czech Republic
Email: david.prochazka.km@vse.cz
Marzena Starnawska
Centre for Entrepreneurship,
Faculty of Management,
University of Warsaw,
Szturmowa 1/3, 02-678 Warsaw, Poland
Email: mstarnawska@wz.uw.edu.pl
Abstract: One of the most important questions of individuals pursuing
entrepreneurship as a career choice is, do entrepreneurs earn more, compared to
employees? We aim to contribute to this research by comparing earnings of
employees, solo-self-employed and self-employed with employees. We utilise
data from the two waves of the European Survey on Working Conditions
(2010, 2015) and we focus on Visegrad countries (Czech Republic, Hungary,
Poland and Slovakia). The analysis is based on OLS approach and nearest
neighbour matching approach. Controlling for key individual characteristics,
we find positive returns to entrepreneurship. However, we show that the OLS
approach over-estimates the size of returns to entrepreneurship, compared to
matching approach. We find that self-employed without employees earn on
average 6.7% more when compared to employees, and to self-employed with
employees even on average 22% more than employees. We also find that job
creators earn on average 22% more when compared with solo-self-employed.
Keywords: returns to entrepreneurship; employees; self-employed with
employees; solo-self-employed; European Survey on Working Conditions;
EWCS; income.
518 O. Dvouletý et al.
Reference to this paper should be made as follows: Dvouletý, O.,
Procházka, D.A. and Starnawska, M. (2021) ‘Who earns more: job creators,
solo-entrepreneurs or employees? Empirical evidence from Visegrad
countries’, Int. J. Entrepreneurship and Small Business, Vol. 43, No. 4,
pp.517–530.
Biographical notes: Ondřej Dvouletý works at the Department of
Entrepreneurship, University of Economics in Prague, where he also obtained
his PhD degree. His research is dedicated to the investigation of entrepreneurial
activity, effects of public entrepreneurship and self-employment policies and
entrepreneurial economics. He also actively contributes to the development of
the academic community. He is an editor of the annual Innovation
Management, Entrepreneurship and Sustainability (IMES) conference and he is
also a member of the editorial advisory board of the Journal of
Entrepreneurship in Emerging Economies and other journals. Besides that, he
serves as a reviewer for several scientific journals and conferences.
David Anthony Procházka is based at the Department of Management,
University of Economics in Prague and Center for Science and Research at the
same university, where he started Research Club. His research interests include
management, entrepreneurship and education. He studied entrepreneurship at
the University of Cambridge, Judge Business School. He is a Fellow of Center
for Evidence-Based Management and a member of the organisational and
programme committee of the annual Innovation Management, Entrepreneurship
and Sustainability (IMES) conference.
Marzena Starnawska works as a Senior Research Fellow at the Faculty of
Management Centre for Entrepreneurship, University of Warsaw, Poland. Her
research interest is focused on entrepreneurship, social entrepreneurship and
societal aspects of entrepreneurship. She obtained her PhD degree at Gdańsk
University of Technology (GUT), Poland. Previously, she worked as a Lecturer
at GUT and in Scotland, Aberdeen Business School, the RGU. She acts as a
member of the editorial board for internationally recognised entrepreneurship
journals, and she serves for EURAM board and SIG Entrepreneurship
Programme. She acts as a reviewer for several journals and conferences.
1 Introduction
One of the most important questions of individuals pursuing entrepreneurship as a career
choice is if entrepreneurs earn more when compared to employees. Is there a premium for
undertaking the risks of self-employment? Unfortunately, the answer to this fundamental
research question is not clear as there is a significant lack of research in differences in
earnings distributions of entrepreneurs and paid employees (e.g., Berkhout et al., 2016;
Carter, 2011; Halvarsson et al., 2018; Hurst and Pugsley, 2016; Levine and Rubinstein,
2017; Manso, 2016; Van Praag and Raknerud, 2017; Kim, 2018).
Some evidence indicates that entrepreneurs experience higher earnings growth (once
their businesses survive the first years) compared to employees at the beginning of their
career; however, these studies do not find any difference in their actual incomes (Brock
et al., 1986; Borjas and Bronars, 1989; Evans and Leighton, 1989; Holtz-Eakin et al.,
1994; Rees and Shah, 1986). Moreover, the previously published studies show that many
individuals are willing to start their own enterprises and stay self-employed in spite of
Who earns more: job creators, solo-entrepreneurs or employees? 519
their wages being significantly lower in comparison of their wage as the employee
(Hamilton, 2000). Hamilton (2000), in his pioneering work, compared the median wages
of employees and self-employed, and he found that self-employed earned significantly
less. Estimates of the average difference in returns to entrepreneurship are –7.5% in
Germany (Sorgner et al., 2017), –9% (Hartog et al., 2010) and –15% (Tergiman, 2010) in
the USA, but also +16% in Norway (Berglann et al., 2011). This is in contrast to earlier
theories (Evans and Jovanovic, 1989; Kihlstrom and Laffont, 1979; Knight, 1921;
Lucas, 1978; Taylor, 1996) that suggest that people seek and select their occupation in
accordance with its expected utility and therefore choose and pursue the way of highest
return, being in employment or entrepreneurial path.
Entrepreneurs also work longer hours in comparison to employees (Ajayi-Obe and
Parker, 2005; Blanchflower, 2004; Hurst et al., 2014; Hyytinen et al., 2013). One of the
prevailing questions based in this body of research is why do people leave their jobs to
become entrepreneurs when generally their prospect is to earn less and work more
hours, not mentioning higher risks involved? (Åstebro and Chen, 2014). Scholars (e.g.,
Van Praag and Raknerud, 2017) explain this classical entrepreneurship earnings puzzle
by answers from entrepreneurship surveys (such as Global Entrepreneurship Monitor),
where individuals talk about their motivation to pursue an entrepreneurial career.
According to these observations (e.g., Barba-Sánchez and Atienza-Sahuquillo, 2017;
Burke et al., 2002; Holienka et al., 2017; Millán et al., 2013; Van Gelderen and Jansen,
2006; Marshall and Gigliotti, 2018; Lukeš and Zouhar, 2013; Parker, 2004), the most
frequently mentioned motive for business start-up is to become one’s own boss and to
become independent, and other non-pecuniary benefits rather than pecuniary ones. So
even though it is broadly assumed that individuals engage in entrepreneurship to gain
profits, there are other parallel claims about it as non-profit seeking activity (Benz, 2009).
Åstebro and Chen (2014) also offer possible explanation related to technical difficulties
with data gathered for these relevant studies.
We also need to point out that there are differences between different types of
entrepreneurs, concerning their growth aspirations and their willingness to create new
jobs (Bögenhold and Klinglmair, 2016; Dvouletý, 2018; Fuchs, 1982; Halvarsson et al.,
2018; Hechavarría et al., 2017). Especially, the fundamental distinction about those
entrepreneurs who create jobs and those staying solo, has not received much attention in
the literature yet due to data availability which is a pity. Dvouletý (2018) in his recent
study finds that there are considerable differences concerning both groups in variables
such as age, education, and household situation when it comes to job creation. He finds
that job creators are usually middle-aged individuals who on average work more hours,
have more experience and possess higher levels of education. This corresponds with
earlier findings of Petrescu (2016), Millán et al. (2015) and Cowling et al. (2004) who
claim that self-employed with and without employees are different individuals with
different motivations and goals.
Sorgner et al. (2017) and Hamilton (2000) have already shown that income variation
between entrepreneurship and paid employment is much higher. Some entrepreneurs
become business superstars (Sorgner et al., 2017), others struggle to pay for their living
(Mühlböck et al., 2018). Some are pushed to entrepreneurship, and others are pulled
(Amit and Muller, 1995; Dawson and Henley, 2012; Zgheib, 2018). Therefore, it is
important to study also the different types of entrepreneurs. Researchers (e.g., Van Praag
and Raknerud, 2017) also point out that more empirical research is needed in this regard.
520 O. Dvouletý et al.
Our study aims to contribute to this research from the perspective of Central Eastern
European post-communist economies, Visegrad countries (Czech Republic, Hungary,
Poland and Slovakia) that have formerly experienced the process of economic transition.
It can be assumed that obtained findings might be slightly different, compared to
established economies, because of the historical influence of the former socialist regime
on the current economic decision-making, nature and motivations for entrepreneurial
activity (e.g., Cieślik and van Stel, 2014; Dvouletý, 2017a; Dvouletý et al., 2018; Mets
et al., 2018; Nowiński et al., 2019; Kshetri, 2009; Sauka and Chepurenko, 2017;
Smallbone and Welter, 2017; Dana, 2000; Dana and Ramadani, 2015). Entrepreneurial
activity in the Visegrad region is dominated by small and medium-sized enterprises
(SMEs). According to Global Entrepreneurship Monitor (2017), on average, 5.9% of
economically active inhabitants, were active in owning, managing and running a
business, receiving payments for more than 42 months and 8.5% were either a founder
or owner of a nascent business activity somewhere between 2001–2015 (Global
Entrepreneurship Monitor, 2017; Dvouletý, 2017b).
Contrary to the previously published studies comparing all types of entrepreneurs in
one pool with employees, we contribute to the debate by considering in our comparison
at least two fundamental types of self-employed, i.e., job creators (self-employed with
employees) and solo-self-employed (both compared separately with paid employees as a
reference group). This study also has interesting methodological implications for the
research community because it is based on the two empirical approaches. First, we
compare the earnings of self-employed by estimating OLS regressions, and then we
implement the nearest neighbour matching approach to obtain more robust estimates. In
the following part of the article, we describe the data from the two recent harmonised
waves of the European Survey on Working Conditions (2010 and 2015), then we explain
our empirical approach and interpret the obtained estimates. In the final part of the article,
we discuss our findings and provide implications for the research.
2 Data
To achieve our goal, we utilise the two recent harmonised waves of the European Survey
on Working Conditions (2010 and 2015) that is being managed by the European
Foundation for the Improvement of Living and Working Conditions (2018)1 since 1991
in 35 countries. We have tried to use as many relevant variables as possible to compare
earnings across the occupations. Table 1 shows the list of variables we use for the
analysis together with their definitions and Table 2 reports the summary statistics.
Table 1 List of variables
Variable Definition
Employment status Employment status as one of three categories:
• Job creators/self-employed with employees (having at least
one employee excluding the owner of the business)
• Self-employed without employees
• In paid employment (employee)
Who earns more: job creators, solo-entrepreneurs or employees? 521
Table 1 List of variables (continued)
Variable Definition
Net monthly income Respondent’s average recent income in EUR
Age Respondent’s age
Female Dummy variable which equals 1 if the respondent is female
Years of experience Respondent’s years of experience in the current company or
organisation
Worked hours Respondent’s working hours per week
Education Set of dummy variables according to ISCED 7 classification
Migrated Dummy variable which equals 1 if the respondent was not born
in the country of the survey
Living alone Dummy variable which equals 1 if the respondent was living in
his/her household alone
Year of survey Year when the survey was conducted
Country Respondent’s country of residence
Industry (NACE codes) Respondent’s work industry classification according to NACE
codes
Occupation (ISCO 1 codes) Respondent’s occupation according to ISCO 1 classification
Table 2 Summary statistics
Variable Frequency (%) N
Self-employed with employees (= 1) 4.0 7,937
Self-employed without employees (= 1) 11.6 7,937
Female (= 1) 47.4 8,064
Pre-primary education (= 1) 0.0 8,033
Primary education or first stage of basic education (= 1) 1.5 8,033
Lower secondary or second stage of basic education (= 1) 6.7 8,033
(Upper) secondary education (= 1) 64.1 8,033
Post-secondary non-tertiary education (= 1) 6.2 8,033
First stage of tertiary education (= 1) 20.8 8,033
Second stage of tertiary education (= 1) 1.0 8,033
Migrated (= 1) 2.3 8,048
Living alone (= 1) 8.8 7,992
Variable Mean SD N
Net monthly income in EUR 534.41 384.61 5,362
Age 43.58 12.31 7,968
Years of experience 9.17 8.98 7,713
Worked hours 39.96 12.33 7,633
Notes: Self-employed and employed only. Post-stratification weights applied.
522 O. Dvouletý et al.
3 Empirical approach and results
Our study strives to explore the differences in earnings of employers (self-employed with
employees), solo-entrepreneurs and employees. In line with the previous estimates of
earnings functions (Card, 1999; Mincer and Polachek, 1974; Polachek, 2008), we have
transformed our earnings variable into a form of natural logarithm. Methodologically, we
follow two empirical designs. We believe that employing more empirical approaches
would help us to obtain more reliable results (Dana and Dana, 2005; McDonald et al.,
2015). We begin with the estimation of a set of OLS regressions, where we compare
earnings of these three groups, and then we employ the nearest neighbour matching
technique, to obtain even more precise estimates. Moreover, our estimates are controlled
for the traditional determinants of entrepreneurship that have been widely studied in
the literature (Dvouletý, 2018; Van der Zwan et al., 2016; Simoes et al., 2016), and
those available in our dataset (age, gender, education, household situation, migration
background, worked hours and years of experience).
3.1 OLS approach
Considering all described variables in Table 1 that might have influenced individual
earnings, we estimate OLS regressions with the dependent variable of the natural
logarithm of earnings. The presented models in Table 3, have been estimated with
robust standard errors (and also bootstrapped 1,000 times), they do not suffer from
collinearity issues [based on variance inflation factors (VIF) test], and they meet standard
econometric assumptions (Wooldridge, 2002). Estimated models are controlled for
industry and occupational dummies, and they also include country and year dummies.
Table 3 Robust OLS regression estimates
Model Model (1) Model (2) Model (3)
Independent/dependent variables Log (net monthly income in EUR)
Age 0.0412*** 0.0390*** 0.0533**
(0.00521) (0.00537) (0.0190)
Age squared –0.000518*** –0.000490*** –0.000703**
(0.0000620) (0.0000643) (0.000222)
Female –0.193*** –0.194*** –0.122
(0.0173) (0.0161) (0.0675)
Years of experience 0.0136*** 0.0182*** –0.0230
(0.00290) (0.00278) (0.0127)
Years of experience squared –0.000210* –0.000318*** 0.000710
(0.0000917) (0.0000890) (0.000383)
Worked hours (per week) 0.0148*** 0.0167*** 0.00869***
(0.000995) (0.00111) (0.00200)
Pre-primary education (.) (.) (.)
(.) (.) (.)
Notes: Standard errors in parentheses: *p < 0.05, **p < 0.01 and ***p < 0.001.
The estimates have been bootstrapped with 1,000 replications.
Who earns more: job creators, solo-entrepreneurs or employees? 523
Table 3 Robust OLS regression estimates (continued)
Model Model (1) Model (2) Model (3)
Independent/dependent variables Log (net monthly income in EUR)
Primary education or first stage
of basic education
–0.272 –0.173 –0.607
(0.412) (0.325) (0.489)
Lower secondary or second stage
of basic education
–0.128 –0.0229 –0.600
(0.406) (0.318) (0.470)
(Upper) secondary education –0.0105 0.0942 –0.399
(0.405) (0.318) (0.440)
Post-secondary non-tertiary
education
0.0792 0.184 –0.340
(0.409) (0.319) (0.447)
First stage of tertiary education 0.313 0.405 0.0126
(0.407) (0.319) (0.430)
Second stage of tertiary education 0.532 0.680* (.)
(0.424) (0.335) (.)
Migrated –0.0259 0.0291 0.0363
(0.0429) (0.0415) (0.170)
Living alone 0.0287 0.0234 0.150
(0.0198) (0.0201) (0.0791)
Self-employed without
employees
0.0936*
(0.0379)
Self-employed with employees 0.234*** 0.252***
(0.0568) (0.0642)
Constant 4.954*** 4.802*** 5.561***
(0.445) (0.364) (0.597)
Year dummies Yes Yes Yes
Country dummies Yes Yes Yes
Industry dummies (NACE codes) Yes Yes Yes
Occupational dummies (ISCO 1
codes)
Yes Yes Yes
Sample description
Employed and
self-employed
without employees
Employed ands
self-employed with
employees
Self-employed
Observations 4,830 4,552 520
R2 0.427 0.456 0.446
Adjusted R2 0.422 0.451 0.400
AIC 6,612.8 5,829.7 1,038.7
BIC 6,898.0 6,112.3 1,213.1
Notes: Standard errors in parentheses: *p < 0.05, **p < 0.01 and ***p < 0.001.
The estimates have been bootstrapped with 1,000 replications.
524 O. Dvouletý et al.
In Models 1 and 2, we compare self-employed with and without employees with the
reference group of employees. The control variables in these two models seem to be
generally in line with the classical economic theory of human capital, showing that the
there is a nonlinear pattern between age, experience and earnings (Becker, 1994; Mincer
and Polachek, 1974). Both models indicate also indicate that females earn less, and quite
intuitively, the monthly earnings grow with the number of worked hours. Surprisingly,
we failed to find any significance for the variables measuring education, migration status
or living alone in a household (Bental et al., 2017; Card, 1999; Mincer and Polachek,
1974; Polachek, 2008). When it comes to the main variable of interest, the returns
to entrepreneurship/self-employment, the results show that self-employed without
employees (solo-entrepreneurs) earn on average 9.4% more compared to employees and
self-employed with employees (employers) even on average 23.4% more than employees.
Initially, it seems that job creators earn more than solo-entrepreneurs, and thus, we
wanted to see, if the differences are significant. Therefore, we estimated the model only
on the sample of self-employed. The results have shown, that employers earn on average
25.2%, compared to solo-entrepreneurs.
3.2 Nearest neighbour matching approach
Inspired by the scholars in the field (e.g., Van Praag and Raknerud, 2017), we also
implement matching techniques to pair entrepreneurs and employees based on the most
similar characteristics we can observe [for details about the application of the matching
procedures, see e.g., Khandker et al. (2009)]. In our case, we select for the covariates
all variables we have in the previous regression (see again Table 3) and follow the
established practice and apply the nearest matching algorithm with up-to-five
counterparts (neighbours) to see if the results will be different from the OLS estimates.
Once we run the matching algorithm based on the set of covariates, we need to make sure
that our matching is successful. The matching diagnostics are reported in Table 4 for all
three samples. Based on the size of the mean and median bias and the test of LR chi2, we
can conclude that there are no significant differences between both groups, based on the
selected characteristics (Khandker et al., 2009; Shakhnarovich et al., 2006).
Table 4 Nearest neighbour (5) matching quality diagnostics
Sample (group) Sample Ps R2 LR chi2 p > chi2 Mean
bias
Median
bias
Employed and self-employed
without employees
Unmatched 0.225 616.86 0.00 17.3 15.6
Matched 0.017 18.82 1.00 4.1 3.6
Employed and self-employed
with employees
Unmatched 0.285 314.87 0.00 20.0 12.6
Matched 0.024 7.77 1.00 4.8 4.4
Self-employed only Unmatched 0.194 106.76 0.00 15.1 12.9
Matched 0.029 9.67 1.00 4.3 3.1
Source: Own calculations
Therefore, we might proceed towards the final estimates that are shown in Table 5.
Contrary to the results reported by OLS, the matching estimates show slightly lower
values of returns to entrepreneurship. However, all differences have been found to be
statistically significant and positive. The results show that self-employed without
Who earns more: job creators, solo-entrepreneurs or employees? 525
employees earn on average 6.7% (OLS reported 9.4%) more compared to employees and
self-employed with employees (employers) even on average 22% (OLS reported 23.4%)
more than employees. Finally, once we compared solo-self-employed and entrepreneurs
having employees, the results have shown, that employers earn on average 22% (OLS
reported 25.2%), compared to solo-entrepreneurs.
Table 5 Estimated returns to self-employment based on nearest neighbour (5) matching
Sample (group) Outcome
group
Estimated
effect Std. error P > abs. Z N
Employed and self-employed
without employees
Solo 0.067* 0.03 0.03 4,784
Employed and self-employed
with employees
Employers 0.22*** 0.08 0.01 4,364
Self-employed only Employers 0.22*** 0.07 0.00 496
Notes: Stat. significance: *p < 0.05, **p < 0.01 and ***p < 0.001. Bootstrapped standard
errors with 100 replications were used for all estimates together with common
support option.
Source: Own calculations
4 Discussion and concluding remarks
Our study aimed to enrich the academic literature on the returns to entrepreneurship from
the perspective of the four post-communist economies associated in the Visegrad group,
namely the Czech Republic, Hungary, Poland and Slovakia. Contrary to the previously
published studies on this topic, we have incorporated a distinction between those
self-employed having employees and solo-self-employed. We have also implemented
two empirical approaches towards the data analysis.
The study is based on data from the European Survey on Working Conditions (2010
and 2015), and we have analysed the differences in net monthly earnings of job creators
(self-employed with employees), own-account workers (solo-self-employed) and paid
employees. Empirically, we have employed OLS regressions and nearest neighbour
matching approach to obtain more robust estimates. Our results show that OLS
approach over-estimates the size of the returns of entrepreneurship, and therefore we
methodologically rely more on the matching approach.
Contrary to the previously published studies (Hartog et al., 2010; Sorgner et al., 2017;
Tergiman, 2010), we find positive returns to entrepreneurship. Self-employed without
employees earn on average 6.7% more, compared to employees and self-employed with
employees (employers) even on average 22% more than employees. Comparison of
self-employed with and without employees showed that job creators earn on average 22%
more, compared to solo-self-employed. These findings might be useful for individuals
considering an entrepreneurship career in the Visegrad region, because there are on
average positive financial gains for choosing entrepreneurial career.
Nevertheless, our study is not without limitations. Data from the European Survey
on Working Conditions (2010 and 2015) is limited by the number of observations and
self-reported data, and thus, more robust dataset would be definitely needed to answer
the research question fully. The limited dataset has also refrained us from the more
526 O. Dvouletý et al.
sophisticated analysis that would identify differences in earnings concerning different
levels of education, years of experience, gender or industry. We recommend future
researchers to consider these differences in the forthcoming studies. Future research
should pay attention also to the detailed taxonomy of self-employed as there are
significant differences between different types of entrepreneurs.
We also need to admit, that the data collected from the European Survey on Working
Conditions are self-reported and thus, the respondents might under/over report their real
incomes. Having administrative data obtained from the social security systems and from
financial authorities might at least partially solve this issue (even administrative data
might be biased due to taxation). Therefore, we recommend future research to also take
into account net earnings but also earnings before taxation. From the methodological
point of view, we recommend researchers to follow the matching approach rather than the
OLS approach.
Acknowledgements
This work was supported by the Internal Grant Agency of Faculty of Business
Administration, University of Economics in Prague, under no. IP300040.
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Notes
1 For questionnaires, details, sampling and data collection procedures, see Eurofound (2017).
